Using the dataset, we compare ATTN-MEAN with an LSTM autoencoder (LSTM-AE), Convoy [1]}, Swarm [2]}, T2VEC [3]}, BFEA [4]}.
For two pattern mining based methods Convoy and Swarm, we compare the number of extracted clusters with that discovered using our learned representations. To comply with the two algorithms, we conduct linear interpolations in the dataset, such that we generate a synthetic point every 10 seconds for each trajectory, if necessary. For T2VEC and BFEA, we also inject positional encodings into their original input for a fair comparison. We use DBSCAN to cluster the learned encodings and compare the clustering performance. We only list the main results due to page limit. More experimental results are available upon request.
